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Navigating the data intersection of commercial plans and government-funded programs to detect and prevent health care fraud, waste and abuse

Many health care industry pundits speculate that traditional fee-for-service (FFS) payment models in government-sponsored and commercial plans will soon go the way of the dinosaur. The US Department of Health and Human Services validated this notion at the federal level when it recently set a timeline to move 30 percent of Medicare payments away from FFS by 2016 and 50 percent by 2018. What’s replacing them? Bundled-payment and/or value-based contract models such as accountable care organizations, patient-centered medical homes and/or managed care organizations.

The industry experts tend to agree that these new payment models can lead to higher-quality care, lower costs and improved experiences for patients and providers. But what about the insidious health care fraud and improper payments paid to providers valued at $100 billion per year? Will the new models help prevent fraud and improper payments?

Most likely, no – because fraudsters will simply adapt to take advantage of the new types of incentives built into these models. The challenge for both government-sponsored programs and commercial plans will be staying one step ahead of the traditional “FFS Bad Guy.”

The right data management, integration and quality infrastructure will be supported by a robust business analytics foundation – because in this new world, staying competitive will be defined by one’s ability to gain insights from data.

The new face of health care fraud and payment integrity challenges

In the FFS world, “creative billers” would simply add extra services or supplies that were never rendered to their bill, up-code to show a higher level of service than was actually performed, and/or order unnecessary procedures. In the new world, unscrupulous health care providers will falsify documentation in new ways to show that:

A provider had more patient encounters than they actually had.

A provider showed better-quality outcomes than their patients actually had.

A provider’s mix of patients is higher-risk than is normal, which would typically entitle them to higher reimbursements.

The point is, corrupt people and organizations will still be able to manipulate the facts in claims for reimbursement – and in ways that are difficult to detect using traditional IT resources and processes. The question is, how should government agencies (like CMS and Medicaid state agencies) and commercial plans prepare so they can detect and prevent provider fraud and improper payments? What new sources of data will they need to collect and analyze to detect it?

Responding to threats with data management and analysis

Government-sponsored programs and commercial plans will need a data management infrastructure that provides access to data across programs, products and channels, as well as the analytical tools to detect health care fraud and improper payments hidden in this data. And despite what many rip-and-replace vendors will say, this won’t require a database overhaul or a massive central data warehouse, but rather a data integration layer that can source from databases around the organization, business partner organizations, social media outlets, and external public or purchased data. They will also need data quality functions that support entity resolution, as unscrupulous providers and suppliers often intentionally provide inaccurate, incomplete or inconsistent information to prevent records matching across disparate systems.

So, what will this look like in the “new payment frontier”? The right data management, integration and quality infrastructure will be supported by a robust business analytics foundation – because in this new world, staying competitive will be defined by one’s ability to gain insights from data. With the bottom-line and quality outcomes in mind, these insights will also help identify suspicious data patterns that could point to programmatic health care fraud, waste or abuse.

To successfully analyze vast amounts of granular data, this data infrastructure must be able to:

These data management capabilities are not only critical to everyday business functions, but also to detecting and preventing health care fraud and improper payments. The volume, variety and velocity of data will keep growing, increasing the gap between relevant data and big data. And for organizations without these capabilities, this will create an uncertain amount of information overload that will undoubtedly make the job of reducing the time to detection and time of action even more difficult than it was in the FFS environment. So government and commercial players will need to develop them in-house, or partner with an organization with expertise in this area to provide these capabilities to ensure success in this new payment frontier.

Ricky D. Sluder, CFE, is a Government Fraud Solution Specialist in the Security Intelligence Practice at SAS. He has 18 years of investigative experience in white collar crime, Medicare and Medicaid fraud, waste and abuse. In 2012, five major cases identified under his investigative/data analytic operational model resulted in DOJ criminal prosecutions exceeding $846 million in fraud.